Module Provider: APD
Number of credits: 20 [10 ECTS credits]
Terms in which taught: Autumn / Spring term module
Non-modular pre-requisites:
Modules excluded:
Current from: 2019/0

Module Convenor: Dr Ariane Kehlbacher

Email: a.kehlbacher@reading.ac.uk

Type of module:

Summary module description:

This module will provide students with the ability estimate different types of econometric models using different types of data to answer questions in economics and other social sciences. The module covers econometric models that can deal with different types of dependent variables (continuous, categorical, censored), and different types of data (cross-section and time-series). Students learn how to specify appropriate models that allow them to answer specific research questions. They also learn how carry out different types of hypothesis testing and how to critically interpret the results. At the end of the module students will be able to translate data into models to make forecasts and to support decision making in a wide variety of fields, ranging from microeconomics to finance and marketing.

The prerequisites for this course are familiarity with elementary mathematics and statistics.


This module provides an introduction to different econometric models as applied to different types of data. At the end of this module students should be able to

  • translate different types of data into appropriate econometric models to make forecasts and to support decision making

  • specify model such that they can be used to answer a specific research questions

  • conduct hypothesis testing and interpret results critically

  • be aware of and familiar with various type of shortcoming in data and how to address them

  • handle data sets and use the software Gretl to carry out econometric analysis of different types of data using different types of models

Assessable learning outcomes:

At the end of the modules, students should be able to:

  • Understand how to specify and estimate various econometric models applied to different types of data (cross-section, time series)

  • Interpret and critically evaluate results obtained from a variety econometric models applied to different types of data

  • Be aware of common problems associated with different types of data and methods of addressing them

  • Combine data handling skills and econometric software skills to undertake applied econometric analysis and evaluate and interpret results

Additional outcomes:

Outline content:

Autumn Term

  1. Probability Theory I

  2. Probability Theory II

  3. Simple regression Models

  4. Multiple Regression Models I

  5. Multiple Regression – Application

  6. Multiple Regression Models II

  7. Single & joint restrictions

  8. Hypothesis Testing – p-values

  9. Logistic regression

  10. Logistic Regression – Application

Spring Term

  1. Coursework assignment

  2. Model specification I

  3. Model specification II

  4. Model specification – Application

  5. Endogeneity & Instrumental Variables

  6. Heteroscedasticity & Autocorrelation

  7. Time Series

  8. Time Series – Application

  9. Ordered logistic regression + Tobit model

  10. Ordered logistic regression + Tobit model – Application

Brief description of teaching and learning methods:

Lectures will provide an understanding of fundamental concepts and demonstrate the use of data analysis methods. Practical classes will involve students analysing real data sets with a focus on learning the concepts taught in the lectures.

Contact hours:
  Autumn Spring Summer
Lectures 20
Tutorials 10
Practicals classes and workshops 20
Guided independent study: 150
Total hours by term 200
Total hours for module 200

Summative Assessment Methods:
Method Percentage
Written assignment including essay 50
Report 35
Class test administered by School 15

Summative assessment- Examinations:

Summative assessment- Coursework and in-class tests:

Number and length of assignments and in-class tests, and submission date for each assignment (expressed as a week of a specific Term):

  • In-class tests - 15 minutes – autumn term, week 7

  • 1 Report (1,500 words) – autumn term

  • 1 Coursework (2,500 words) – spring term

Formative assessment methods:

Penalties for late submission:
Penalties for late submission on this module are in accordance with the University policy. Please refer to page 5 of the Postgraduate Guide to Assessment for further information: http://www.reading.ac.uk/internal/exams/student/exa-guidePG.aspx

Assessment requirements for a pass:
A mark of 50% overall.

Reassessment arrangements:
Coursework assignment to be carried out in August.

Additional Costs (specified where applicable):
1) Required text books:
2) Specialist equipment or materials:
3) Specialist clothing, footwear or headgear:
4) Printing and binding:
5) Computers and devices with a particular specification:
6) Travel, accommodation and subsistence:

Last updated: 23 April 2019


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